Key highlights
- AspenTech’s software aligns with 12 sustainability pathways identified by the U.N. COP and IEA, offering process engineers concrete tools to support key environmental targets.
- Tools like Aspen Unified Reconciliation and Accounting (AURA) provide mass-balance-based, auditable emissions tracking.
Beyond optimizing routine operations, Aspen Technology Inc. reports that its software helps users and their organizations address the goals identified by the U.N.’s Conference of the Parties (COP) and the International Energy Agency.
To focus software innovation within AspenTech and help companies use the technology effectively, AspenTech has identified 12 sustainability pathways that technology contributes to, including:
- Energy efficiency,
- Emissions management,
- Electrification,
- Water conservation,
- Waste reduction,
- Bio-based feedstocks,
- Hydrogen economy,
- Renewable energy,
- Carbon capture and storage,
- New materials,
- Plastics circularity, and
- CO2 as a feedstock.
“There are diverse sustainability strategies being pursued, from short-term efficiency gains to long-term transitions to alternative energy sources. However, each contributes to reducing emissions and increasing efficiency,” says Ron Beck, senior solutions marketing director at AspenTech. “We’ve focused on sustainability for years, but we’re still surprised by how much more there is that we can do. For example, companies can fairly easily achieve a detailed situational awareness of where operations adjustments can achieve added savings on fuel and energy, and by reducing emissions and waste. These include simple calculations about fuel consumption and the carbon footprints of energy sources, based on existing data, so users in plants and their management can decide where to spend to reduce emissions the most.”
Tracking emissions in Italy and elsewhere
Beck reports that AspenTech and a group of collaborating customers, developed detailed, real-time emissions tracking using Aspen Unified Reconciliation and Accounting (AURA) software that’s been used for years to measure and track hydrocarbons in assets and facilities. Its accounting method employs a mass-balance approach, so users can accurately calculate what they’re actually using and spending. AURA’s latest release in November 2023 added a carbon-accounting capability, which resulted from its collaboration. That system has been implemented at one collaborator’s facility, the SARAS refinery in southern Italy, where it’s expected to help SARAS avoid potentially millions of Euros in carbon emission levies.
“These accurate tools and models are necessary because users typically need auditable measurements or other results to understand potential fines, credits and progress towards committed goals, especially when they begin using alternative energy sources to produce bio-feedstocks or capture carbon,” explains Beck. “For instance, Climeworks’ Mammoth facility is using Iceland’s geothermal energy and technology to help perform direct-air carbon capture. Investors and buyers of credits from projects like Climeworks need the confidence that the project is achieving the results they’ll be taking credit for. Likewise, the U.S. Inflation Reduction Act is funding many solar and wind projects, such as CarbonCapture Inc.’s Project Bison in Wyoming, which combines alternative energy sources for carbon capture. The economics of that project improved by using AspenTech’s models.”
Dynamic modeling manages variability…
Beck adds that China-based Envision Group presented recently at AspenTech’s Optimize conference in Houston on how it uses AspenTech’s dynamic modeling and AI software to deal with the intermittent availability of alternative energy sources, and apply dynamic control to reduce variability experienced by their wind turbines and other devices.
“For example, dynamically modeling an ammonia process can identify an advanced process control (APC) strategy that’s 10-20% more effective across a wider range of load factors, which can reduce the need for storage buffering of ammonia or renewable power,” explains Beck. “If a user has prepared 20 control regimes they could implement, this modeling, combined with AI-powered weather forecasting, can guide operators about to which strategy will be the most effective and when.
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“The overall sustainability pie is huge, so there’s less need to be secretive because there’s plenty of business to go around, and benefits to all parties if industry improves its sustainability performance. The real question is who can succeed at solving these big problems. For instance, SOCAR targeted their acrylonitrile (ACN) process, which consumes a lot of energy, as part of their corporate goal to reduce greenhouse gas emissions by 35% by 2030. With AspenTech’s modeling software that combines engineering first-principles with AI, SOCAR developed an end-to-end model of the ACN process, equipment design and operating limits. They could evaluate all variables simultaneously to determine the best way to operate the facility to maintain production and optimize energy efficiency. SOCAR improved waste recovery by 36% and saw a 5% reduction in overall plant emissions.”
…and steam reforming
To optimize reaction units with complex chemical processes that resisted previous modeling attempts, AspenTech has embedded hybrid models in its Aspen Plus process simulation software. These models apply AI to create correlations between databases about what’s occurring in the reactors, and can save up to 30% on energy consumption, while increasing production yields.
For example, Nissan Chemical Corp. recently sought to optimize steam reforming in its ammonia manufacturing process, but its existing reactor model was limited, and had problems accurately measuring and estimated temperature distributions in the reactor’s furnace. Aspen Hybrid Models combines first-principles simulation models and AspenTech’s domain expertise with AI and analytics algorithms in Aspen Plus. This solution reproduced the reactor’s data faster and more accurately than its former reformer models; automated model calibration with machine learning (ML) and neural network calculations; and reduced operating costs by 1% by optimizing steam input.
To overcome data reliability concerns, the ammonia reactor’s information had to be thoroughly cleaned before it could be fed to the neural network to train the model. After cleaning, Aspen Plus uses the data to create a hybrid model that calculates reaction rates. This is done by using the neural network’s calculation method, which combines temperature, pressure, feed rate and composition as input variables. Not only does it build models twice as fast as traditional methods, but Aspen Hybrid Models is reported to generate a higher correlation coefficient than conventional models.
“Using AspenTech’s hybrid model, we created a model that can reproduce real plant data more accurately than the conventional reformer model,” says Takuto Nakai, production department, Nissan Chemical. “We were able to create a highly accurate model in a short period of time.”
